In a text document search, a user typically enters a query into a search engine. The search engine evaluates the query against a database of indexed documents and returns a ranked list of documents that best satisfy the query. A score, representing a measure of how well the document satisfies the query, is algorithmically generated by the search engine. Commonly-used scoring algorithms rely on splitting the query up into search terms and using statistical information about the occurrence of individual terms in the body of text documents to be searched. The documents are listed in rank order according to their corresponding scores so the user can see the best matching search results at the top of the search results list.
Another evaluation that certain search engines may employ to improve the quality of the results is to modify the rank of the results by a selected ranking function. One exemplary prior art ranking function determines that when one page links to another page, it is effectively casting a vote for the other page. The more votes that are cast for a page, the more important the page. The ranking function can also take into account who cast the vote. The more important the page, the more important their vote. These votes are accumulated and used as a component of the ratings of the pages on the network.
A ranking function is used to improve the quality of the ranking. However, the effectiveness of the ranking function may be affected by the topology of the network. For example, the ranking function using the votes described above may be less effectual in an intranet setting. An intranet is a network that uses some of the same protocols as the Internet, but is accessible only by a subset of users, such as employees of a corporation. The pages of an intranet are not structured or connected exactly like the Internet, and so the relevancy of the results produced by a ranking function may not be reduced as compared to the Internet setting.